Summary of Key Algorithms For Keyphrase Generation: Instruction-based Llms For Russian Scientific Keyphrases, by Anna Glazkova et al.
Key Algorithms for Keyphrase Generation: Instruction-Based LLMs for Russian Scientific Keyphrases
by Anna Glazkova, Dmitry Morozov, Timur Garipov
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the task of keyphrase selection in natural language processing, focusing on adapting existing supervised and unsupervised solutions for the Russian language. The authors investigate the performance of large language models (LLMs) in generating keyphrases for Russian scientific abstracts, comparing zero-shot and few-shot prompt-based methods to fine-tuned and unsupervised approaches. The study also assesses strategies for selecting keyphrase examples in a few-shot setting and presents human evaluation results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to automatically find important words or phrases in text, which can help people quickly understand what a piece of writing is about. Right now, it’s hard to do this for Russian because there aren’t many training datasets available. Researchers have been working on solving this problem using big language models that don’t need as much training data. This paper looks at how well these models work when generating keyphrases for Russian scientific abstracts. |
Keywords
» Artificial intelligence » Few shot » Natural language processing » Prompt » Supervised » Unsupervised » Zero shot
